Self-Organization Promotes the Evolution of Cooperation with Cultural Propagation

In this paper three computational models for the study of the evolution of cooperation under cultural propagation are studied: Kin Selection, Direct Reciprocity and Indirect Reciprocity. Two analyzes are reported, one comparing their behavior between them and a second one identifying the impact that different parameters have in the model dynamics. The results of these analyzes illustrate how game transitions may occur depending of some parameters within the models and also explain how agents adapt to these transitions by individually choosing their attachment to a cooperative attitude. These parameters regulate how cooperation can self-organize under different circumstances. The emergence of the evolution of cooperation as a result of the agent's adapting processes is also discussed

Landscapes and Effective Fitness

The concept of a fitness landscape arose in theoretical biology, while that of effective fitness has its origin in evolutionary computation. Both have emerged as useful conceptual tools with which to understand the dynamics of evolutionary processes, especially in the presence of complex genotype-phenotype relations. In this contribution we attempt to provide a unified discussion of these two approaches, discussing both their advantages and disadvantages in the context of some simple models. We also discuss how fitness and effective fitness change under various transformations of the configuration space of the underlying genetic model, concentrating on coarse-graining transformations and on a particular coordinate transformation that provides an appropriate basis for illuminating the structure and consequences of recombination

A Study of Neo-Austrian Economics using an Artificial Stock Market

An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the 'excess' profits associated with different trading strategies, where excess for an active trading strategy is defined relative to a dynamic buy and hold benchmark. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another ('signal') relative to the standard error of those profits ('noise') and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative 'informational' advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering 'copycat' traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.Neoaustrian economics, Market efficiency, Artificial financial market, Learning, Adaptation

Livelihood strategies in the rural Kenyan highlands

The concept of a livelihood strategy has become central to development practice in recent years. Nonetheless, precise identification of livelihoods in quantitative data has remained methodologically elusive. This paper uses cluster analysis methods to operationalize the concept of livelihood strategies in household data and then uses the resulting strategy-specific income distributions to test whether the hypothesized outcome differences between livelihoods indeed exist. Using data from Kenya’s central and western highlands, we identify five distinct livelihood strategies that exhibit statistically significant differences in mean per capita incomes and stochastic dominance orderings that establish clear welfare rankings among livelihood strategies. Multinomial regression analysis identifies geographic, demographic and financial determinants of livelihood choice. The results should facilitate targeting of interventions designed to improve household livelihoods.Livelihood strategy, Kenya, Smallholder agriculture, Cluster analysis, Community/Rural/Urban Development,

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Evidence of Orbital Motion in the Binary Brown Dwarf Kelu-1AB

We have resolved Kelu-1 into a binary system with a separation of ~290 mas using the Laser Guide Star Adaptive Optics system on the Keck II telescope. We have also re-analyzed a 1998 HST observation of Kelu-1 and find that the observed PSF is best fit by a binary object separated by 45 mas. Observations on multiple epochs confirm the two objects share a common proper motion and clearly demonstrate the first evidence of orbital motion. Kelu-1B is fainter than Kelu-1A by 0.39+/-0.01 magnitudes in the K' filter and 0.50+/-0.01 magnitudes in the H filter. We derive spectral types of L2+/-1 and L3.5+/-1 for Kelu-1A and B, respectively. The separation of flux into the two components rectifies Kelu-1's over-luminosity problem that has been known for quite some time. Given the available data we are able to constrain the inclination of the system to >81 degrees and the orbital period to >~40 years.Comment: 15 pages, 3 figures, Accepted to PASP. Changes include: title, author list, new data, more analysis, and new journa

Vibrational Density Matrix Renormalization Group

Variational approaches for the calculation of vibrational wave functions and energies are a natural route to obtain highly accurate results with controllable errors. However, the unfavorable scaling and the resulting high computational cost of standard variational approaches limit their application to small molecules with only few vibrational modes. Here, we demonstrate how the density matrix renormalization group (DMRG) can be exploited to optimize vibrational wave functions (vDMRG) expressed as matrix product states. We study the convergence of these calculations with respect to the size of the local basis of each mode, the number of renormalized block states, and the number of DMRG sweeps required. We demonstrate the high accuracy achieved by vDMRG for small molecules that were intensively studied in the literature. We then proceed to show that the complete fingerprint region of the sarcosyn-glycin dipeptide can be calculated with vDMRG.Comment: 21 pages, 5 figures, 4 table

The birth place of the type Ic Supernova 2007gr

We report our attempts to locate the progenitor of the peculiar type Ic SN 2007gr in HST pre-explosion images of the host galaxy, NGC 1058. Aligning adaptive optics Altair/NIRI imaging of SN 2007gr from the Gemini (North) Telescope with the pre-explosion HST WFPC2 images, we identify the SN position on the HST frames with an accuracy of 20 mas. Although nothing is detected at the SN position we show that it lies on the edge of a bright source, 134+/-23 mas (6.9 pc) from its nominal centre. Based on its luminosity we suggest that this object is possibly an unresolved, compact and coeval cluster and that the SN progenitor was a cluster member, although we note that model profile fitting favours a single bright star. We find two solutions for the age of this assumed cluster; 7-/+0.5 Myrs and 20-30 Myrs, with turn-off masses of 28+/-4 Msun and 12-9 Msun respectively. Pre-explosion ground-based K-band images marginally favour the younger cluster age/higher turn-off mass. Assuming the SN progenitor was a cluster member, the turn-off mass provides the best estimate for its initial mass. More detailed observations, after the SN has faded, should determine if the progenitor was indeed part of a cluster, and if so allow an age estimate to within ~2 Myrs thereby favouring either a high mass single star or lower mass interacting binary progenitor.Comment: 12 pages, 3 figures, resolution of fig 1. has been reduced, some revision based on referee's comments, Accepted ApJL 27 Nov 200